Controlled-source electromagnetic (“CSEM”) surveys are becoming an important geophysical tool for evaluating the presence of hydrocarbon-bearing strata within the earth. CSEM surveys typically record the electromagnetic signal induced in the earth by a source (transmitter) and measured at one or more receivers. The behavior of this signal as a function of transmitter location, frequency, and separation (offset) between transmitter and receiver can be diagnostic of rock properties associated with the presence or absence of hydrocarbons. A notable diagnostic rock property of this kind is electrical resistivity. Thus, CSEM measurements are typically used to determine the spatially-varying resistivity of the subsurface.
In the marine environment, CSEM data are typically acquired by towing an electric dipole transmitting antenna 10 among a number of receivers 11 positioned on the seafloor 12 (FIG. 1). The receivers typically have multiple sensors designed to record different vector components of the electric and/or magnetic fields. The transmitter antenna is typically towed (by a vessel on the sea surface 13) a few tens of meters above the seafloor. The receivers are weighted and fall to the seafloor, but release their weight and rise back to the surface with the data after receiving an acoustic command from the ship. Alternative configurations include stationary transmitters on the seafloor or in the water column as well as magnetic transmitter antennae. The transmitting and receiving systems typically operate independently (without any connection between them), so that receiver data must synchronized with shipboard measurements of transmitter position by comparing clock times on the receivers to time from a shipboard or GPS (Global Positioning System) standard.
CSEM data are typically interpreted in the temporal frequency domain, each signal representing the response of the earth to electromagnetic energy at that temporal frequency. Temporal frequency domain means the data is transformed, typically by Fourier transformation, such that the dependence of the data on time becomes dependence on frequency. In raw data, the strength of each frequency component varies depending on how much energy the transmitter broadcasts (i.e., the amplitude of each component in the transmitter's frequency spectrum) and on the receiver sensitivity at that frequency. These transmitter and receiver effects are typically removed from the data prior to interpretation, thereby normalizing the receiver data by the transmitted signal and by the receiver sensitivity. FIGS. 2A-B depict raw receiver data 21 together with the transmitter waveform 22 that gave rise to it. FIG. 2A displays measured data on a time scale of several hours while FIG. 2B shows the received signal (and, for reference, the transmitted signal) on a much shorter time scale, comparable to the transmitter signal period, typically between 4 and 32 seconds. (The vertical scale applies only to the receiver signal.)
In practice, the receiver data are usually converted to temporal frequency by dividing (or “binning”) the recorded time-domain data into time intervals (x1, x2, and x3 in FIG. 3A) equal to the transmitter waveform period (FIG. 3A) and determining the spectrum within each bin by standard methods based on the Fourier transform (FIG. 3B). (The phases of the spectral components are not shown.) With each bin is associated a time, typically the Julian date at the center of the bin. Since the transmitter location is known as a function of time, these bins may be interchangeably labeled in several different ways: by Julian date of the bin center; by transmitter position; by the signed offset distance between source and receiver; or, by the cumulative distance traveled by the transmitter relative to some arbitrarily chosen starting point. In general, the received signals are made up of components both in-phase and out-of-phase with the transmitter signal. The signals are therefore conveniently represented as complex numbers in either rectangular (real-imaginary) or polar (amplitude-phase) form. The transmitter signal may be a more complex waveform than that depicted in FIGS. 2B and 3A.
Those skilled in the art of digital signal processing will know of techniques that will decompose time series, such as raw CSEM data, to temporal frequency without explicitly dividing the data into non-overlapping time intervals. In general, a time series may be transformed to the time-frequency domain and the dominant temporal frequencies extracted separately as functions of time. Some methods of transforming data to the time-frequency domain include the Short-Time Fourier Transform (J. Allen, L. Rabiner, “A Unified Approach to Short-Time Fourier Analysis and Synthesis,” Proc. of the IEEE 65, 1558-64, (1977)); the Wavelet Transform (W. C. Lang and K. Forinash, “Time-frequency analysis with the continous wavelet transform,” Am. J. Phys. 66, 794-797, (1998)); the Wigner-Ville transform (E. Wigner, On the quantum correction for thermodynamic equilibrium, Phys. Rev. 40, 749-759, (1932), and J. Ville, “Theorie et application de la notion de signal analytique,” Cables el Transmission, 2A., 61-74, (1948)); the Choi-Williams transform (H. Choi and W. Williams, “Improved time-frequency representation of multicomponent signals using exponential kernels,” IEEE Trans. on Acoust, Speech, and Signal Processing, 37, 862-871, (1989)); and the Bessel method (Z. Guo, L. G. Durand, and H. C. Lee, “The time-frequency distributions of nonstationary signals based on a Bessel kernel,” IEEE Trans. on Signal Proc., 42, 1700-1707, (1994)). The present invention is not limited to any particular method or methods for spectral decomposition of CSEM data to the temporal frequency domain.
The actual source current injected into the earth will generally deviate from an idealized waveform such as waveform 22 shown in FIG. 2B. Furthermore, the amplitude and shape of the actual waveform may be known with varying levels of precision. Accordingly, the normalization of CSEM receiver data (Rnormalized) by the transmitted current may be carried out using any of several approximate methods. For example, the source waveform may be represented by:                a complex spectrum, S0(ω), where ω is the temporal frequency in radians/sec. and S0(ω), is assumed to remain constant from bin to bin. In this case, the receiver signal, R(ω,r), varies with time or offset, r, and would be normalized as        
                                                        R              normalized                        ⁡                          (                              ω                ,                r                            )                                =                                    R              ⁡                              (                                  ω                  ,                  r                                )                                                                    S                0                            ⁡                              (                ω                )                                                    ;                            (        1        )                            a complex spectrum, S1(ω,r), that does vary from bin to bin and the receiver normalized as        
                                                        R              normalized                        ⁡                          (                              ω                ,                r                            )                                =                                    R              ⁡                              (                                  ω                  ,                  r                                )                                                                    S                1                            ⁡                              (                                  ω                  ,                  r                                )                                                    ;                            (        2        )                            the product of a complex spectrum, S2(ω), that depends only on waveform type and a source current, I0, that that is independent of offset. In this case, the receiver signal would be normalized as        
                                                        R              normalized                        ⁡                          (                              ω                ,                r                            )                                =                                    R              ⁡                              (                                  ω                  ,                  r                                )                                                                    I                0                            ⁢                                                S                  2                                ⁡                                  (                  ω                  )                                                                    ;                            (        3        )                            the product of a complex spectrum, S3(ω), that depends only on waveform type and a source current, I(r), that varies with offset. In this case, the receiver signal would be normalized as        
                                                                        R                normalized                            ⁡                              (                                  ω                  ,                  r                                )                                      =                                          R                ⁡                                  (                                      ω                    ,                    r                                    )                                                                              I                  ⁡                                      (                    r                    )                                                  ⁢                                                      S                    3                                    ⁡                                      (                    ω                    )                                                                                ;                                          ⁢          and                ,                            (        4        )                            the product of a complex spectrum, S4(ω), that depends only on waveform type, a source current, I(r), that varies with offset, and a source phase, φ(ω,r), that may vary from bin to bin. In this case, the receiver signal would be normalized as        
                                          R            normalized                    ⁡                      (                          ω              ,              r                        )                          =                                            R              ⁡                              (                                  ω                  ,                  r                                )                                                                    I                ⁡                                  (                  r                  )                                            ⁢                                                S                  4                                ⁡                                  (                  ω                  )                                            ⁢                              ⅇ                                  ⅈ                  ⁢                                                                          ⁢                                      φ                    ⁡                                          (                                              ω                        ,                        r                                            )                                                                                                    .                                    (        5        )            
In addition to the current injected by the transmitter and the electric- and magnetic-field measurements at the receivers, other types of data and metadata (that is, documentation and information about the survey and about measured data) are also of importance to the analysis of CSEM surveys. Examples of metadata include:                ocean conductivity as a function of depth;        seafloor bathymetry;        positions and orientations of the source antenna versus time;        source spectrum;        source waveform period;        receiver locations in three dimensions;        receiver response functions versus frequency (These functions, sometimes referred to as receiver calibrations, may combine several factors, such as the electronic properties of the amplifiers and digitizers as well as the characteristics of the electric and magnetic field antennae and may be measured in a laboratory removed from the site of the CSEM survey.);        receiver configurations (the mapping of receiver antennae and receiver response functions to specific digital values in the recorded media);        receiver orientations in three dimensions;        timing measurements needed to estimate receiver clock drift; and,        format specifications for both field-recorded and processed data.        
CSEM data analysis may optionally involve inversion, which is the computation of one of more electromagnetic properties of the earth (resistivity, permeability, and permittivity) directly from the CSEM data. CSEM inversion is usually performed by iteratively updating an initial estimate of the earth's electromagnetic properties based on the mismatch between the measured data and synthetic data forward-modeled from the earth parameter estimates. See, for example, D. L. Alumbaugh and G. A. Newman, “3-D massively parallel electromagnetic inversion—Part II. Analysis of a cross well experiment”, Geophysical J, Int. 128, 355-363 (1997) and J. J. Carazzone, O. M. Burtz, K. E. Green, D. A. Pavlov, and C. Xia, “Three-dimensional imaging of marine CSEM data”, Extended abstracts of the 75th Annual International meeting, Society of Exploration Geophysics, 575-578 (2005). Because it results in a representation of the subsurface resistivity structures, this process is also known as CSEM imaging.
CSEM processing is organized around the application of distinct processes or tools that carry out some part of the overall data processing sequence or flow. A typical overall processing sequence is illustrated by the flow chart of FIG. 4. Some example tools are: display spectral data, re-format navigation data supplied by third parties, and noise suppression. Certain processing tools, such as data re-formatting and data merging, can often be carried out with little user direction and only minor checks for accuracy and consistency. Other tools, such as signal processing techniques, require the user to specify one or more parameters. The optimal choice of parameters is frequently data-dependent, so the data processor will typically try several parameter combinations before making a final selection. At a higher level, the selection of specific processing tools or steps from among the available options and the application of these tools in a specific sequence are both choices that the data processor must make. Here too, the optimal choices will likely differ among different CSEM surveys.
FIG. 4 depicts many CSEM processing steps, but not necessarily all the steps that would be required for specific data sets. In particular, quality control steps such as visually inspecting data and steps of data re-formatting are left out. Some metadata has been indicated explicitly. Receiver geometry, for example, includes the physical configuration and lengths of the electric and magnetic field antennae. Other metadata, such as the start and end times of particular tow lines, are not shown. Specific steps and their preferred order will vary from project to project.
CSEM surveys can be large and complex. For example, a survey might involve 10 or more tow lines, 90 receivers, and 10 or more discrete frequencies (in the frequency spectrum of a complex source waveform). Therefore, the data processor faces a bookkeeping challenge to ensure that all of the data have been processed consistently and accurately. Additional challenges arise when a group of data processors with varying levels of experience and expertise must coordinate their efforts to efficiently handle multiple data processing projects, as when individuals take vacation or must be devoted to other tasks.
Several published sources have recognized the need to apply particular processes to CSEM data (Ellingsrud, et al., “Remote sensing of hydrocarbon layers by seabed logging (SBL): results from a cruise offshore Angola”, The Leading Edge 21, 972-982, (2002); MacGregor, et al., “The RAMESSES experiment-III. Controlled-source electromagnetic sounding of the Reykjanes Ridge at 57°45′N, Geophys. J. Int. 135, 773-789 (1998); Spies and Frischknecht, “Electromagnetic Sounding”, in Electromagnetic methods in Applied Geophysics, Vol. 2, M. N. Nabighian ed., Society of Exploration Geophysicists, 285426 (1991); and UK Patent Application GB 2,415,511 to Amundsen and Holvik, “Processing Electromagnetic Data”, (2005)). However, the problem of efficient and accurate CSEM data processing has received little attention in the literature. At least one early author has lamented the lack of standardized processing tools and the immaturity of CSEM processing methods (D. Proubasta, “Electromagnetics in Petroleum Prospecting”, The Leading Edge 2, 3640, (1983)).
James Behrens developed a CSEM processing system called SFT6 (J. P. Behrens, “The Detection of Electrical Anisotropy in 35 Ma Pacific Lithosphere: Results from a Marine Controlled-Source Electromagnetic Survey and Implications for Hydration of the Upper Mantle”, Ph.D. Thesis, University of California, San Diego, 2005). These tools are based on CSEM processing techniques used in earlier academic experiments. SFT6 was developed by Behrens as part of several projects, including NSF-funded projects. The SFT6 system (FIG. 5) is a collection of stand-alone tools whose processing parameters and I/O paths are hard-wired. Thus, the data processor must edit the source code to change processing parameters. The SFT6 system does not contemplate data normalizations of the types described in equations (2-5). The normalization method of equation (1) is implemented in the SFT6 tool sft6plot.m. SFT6 does contemplate different receiver data formats, as evidenced by the distinct tools sft6.m and sft6elf.m indicated in FIG. 5. However, because of its hard-wired processing parameters and the absence of consistent internal data formats, the SFT6 system does not provide a platform to process large amounts of CSEM data rapidly and efficiently or to easily incorporate and test new processing techniques.
The execution of multiple steps as parts of an overall seismic data processing flow to be applied to large quantities of seismic field data has been recognized for some time. An example is given on page 56 of “Seismic Data Processing” by Özdo{hacek over (g)}an Yilmaz (Society for Exploration Geophysicists (1987)). Computer programs (“executors”) to create such processing flows and carry them out on both serial and parallel computers are available in commercial products such as the Omega product available from WesternGeco, 300 Schlumberger Drive, Sugar Land, Tex., the Promax product available from Landmark, 2101 CityWest Blvd, Houston, Tex., and the Geocluster product, available from Compagnie Générale de Geophysique, 16430 Park Ten Place, Houston, Tex. As suggested by FIG. 4, the steps in a processing flow correspond to specific processing algorithms or tools available within the executor. Similar concepts are embedded in free software available under limited license, such as Seismic Un*x (J. W. Stockwell, “The CWP/SU: seismic Un*x package”, Computers and Geosciences 25, 415-419, (1999)) and in academic software, such as SIA (I. B. Morozov and S. B. Smithson, Computers & Geosciences 23, 689-606 (1997) and I. B. Morozov, Computers & Geosciences 24, 285-288 (1998)). See also “Integrated Software Framework for Processing of Geophysical Data,” Chubak and Morozov, Computers and Geosciences 32, 767-775 (2006).
Seismic executors, in turn, embody features not previously known in marine CSEM processing software:                graphical user interfaces to specify tools and flows, control their execution, and to examine and catalog their results. This graphical user interface may consist of windows, buttons, menus, and other widgets to control the operation of the software;        a mechanism to accommodate new processing tools, without editing (and thereby risking damage to) the executor code. The same or similar mechanisms can be used to access new tools for development, testing, or production use and to ensure access to older versions of tools;        standardized file and directory structures to organize raw, synthetic, laboratory, and processed data, metadata, and flows. Standardized structures decrease the risk of processing errors, simplify the operation of the computer program, and enable different individuals to more effectively collaborate in the processing effort;        mechanisms to process data on parallel computers, decreasing the overall processing time required by distributing processing tasks among multiple CPUs;        standardized, common data formats, so that data can flow through tools in different orders (as part of different flows), data supplied by outside parties can be incorporated into a project, and processed data can be passed on to interpretation or inversion systems; and,        documentation, in the form of Help Files on specific tools as well as information on using the system itself.        
Some data processing problems have been addressed in both CSEM and seismic software systems. Specifically, both systems provide means to:                reconcile final geometry (navigation) data with source and receiver data based on timing information. In both types of processing, raw navigation measurements must undergo additional analysis after the source and receiver data have already been recorded. Tools of this type must take particular care to handle surveys that were acquired across year-end and leap-day boundaries;        graphically display the action of processing tools on data. This feature is distinct from the Graphical User Interface; and,        achieve portability across different computer hardware platforms and operating systems.        